Text-to-Speech Markup for Paralinguistic Audio Integration
Solution Overview
Problem
Current text-to-speech systems fail to effectively incorporate paralinguistic events, such as emotional expressions and non-speech sounds, into synthesized speech, limiting their naturalness and ability to convey emotional states.
Innovation Solution
A method and system that convert marked-up text into a synthesized stream by differentiating normal text from paralinguistic text using grammar constraints, retrieving and concatenating corresponding audio segments to include paralinguistic events like sighs, coughs, and laughter, and audibly outputting the stream, allowing for emotional expression and variation in speech prosody.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional text-to-speech systems are used, then speech synthesis is simple and straightforward, but paralinguistic events such as emotional expressions and non-speech sounds cannot be incorporated
Solution Approach 1:
The text input is segmented into normal text and paralinguistic text using grammar constraints. The system divides the text stream into vocabulary items, where paralinguistic items are identified and separated from normal speech text, allowing independent processing and retrieval of corresponding audio segments for emotional expressions and non-speech sounds.
Solution Approach 2:
A markup language serves as an intermediary between the text input and the speech synthesis engine. The markup tags (e.g., <say emotion="sad">)</em> act as mediators that carry paralinguistic information through the synthesis pipeline, enabling the system to interpret and render emotional expressions without fundamentally altering the core TTS architecture.
2Reliability
If paralinguistic text is added to enhance emotional expression, then naturalness and emotional conveyance improve, but text processing complexity increases
Solution Approach 1:
Different parts of the text are treated with different processing rules. Normal text follows standard speech synthesis pathways, while paralinguistic text marked with specific tags undergoes specialized processing to retrieve appropriate emotional audio segments. This localized differentiation allows emotional expression enhancement without uniformly complicating the entire text processing pipeline.
Solution Approach 2:
The text is pre-processed to identify and tag paralinguistic elements before the main speech synthesis process. Markup tags are inserted into the text stream in advance, allowing the synthesis engine to anticipate and prepare appropriate emotional audio segments, thereby streamlining the overall processing rather than adding complexity during real-time synthesis.
3Adaptability or versatility
If multiple audio segments are associated with paralinguistic text for emotional variation, then naturalness improves, but audio segment selection complexity increases
Solution Approach 1:
The system varies parameters such as pitch, duration, and intensity of audio segments based on the emotional context specified in the markup. Multiple audio segments associated with the same paralinguistic tag can be selected by adjusting these parameters, allowing emotional variation without requiring completely separate audio files for each nuance, thereby managing complexity through parameter manipulation rather than sheer volume of audio assets.
Data Source
AI summary
Converting marked-up text into a synthesized stream includes providing marked-up text to a processor-based system, converting the marked-up text into a text stream including vocabulary items, retrieving audio segments corresponding to the vocabulary items, concatenating the audio segments to form a synthesized stream, and audibly outputting the synthesized stream, wherein the marked-up text includes a normal text and a paralinguistic text; and wherein the normal text is differentiated from the paralinguistic text by using a grammar constraint, and wherein the paralinguistic text is associated with more than one audio segment, wherein the retrieving of the plurality audio segments includes selecting one audio segment associated with the paralinguistic text.